AICcmodavg-package {AICcmodavg}R Documentation

Model Selection and Multimodel Inference Based on (Q)AIC(c)

Description

Description: This package includes functions to create model selection tables based on Akaike's information criterion (AIC) and the second-order AIC (AICc), as well as their quasi-likelihood counterparts (QAIC, QAICc). Tables are printed with delta AIC and Akaike weights. The package also includes functions to conduct model averaging (multimodel inference) for a given parameter of interest or predicted values. Other handy functions enable the computation of relative variable importance, evidence ratios, and confidence sets for the best model. The present version works with linear models ('lm' class), generalized linear models ('glm' class), linear mixed models ('lme' class), multinomial and ordinal logistic regressions ('multinom' and 'polr' classes).

Details

Package: AICcmodavg
Type: Package
Version: 1.05
Date: 2009-12-04
License: GPL (>=2 )
LazyLoad: yes

This package contains several useful functions for model selection and multimodel inference:

Author(s)

Marc J. Mazerolle <marc.mazerolle@uqat.ca>. Special thanks to T. Ergon for the original idea of storing candidate models in a list.

References

Anderson, D. R. (2008) Model-based inference in the life sciences: a primer on evidence. Springer: New York.

Burnham, K. P., and Anderson, D. R. (2002) Model selection and multimodel inference: a practical information-theoretic approach. Second edition. Springer: New York.

Burnham, K. P., Anderson, D. R. (2004) Multimodel inference: understanding AIC and BIC in model selection. Sociological Methods and Research 33, 261–304.

Mazerolle, M. J. (2006) Improving data analysis in herpetology: using Akaike's Information Criterion (AIC) to assess the strength of biological hypotheses. Amphibia-Reptilia 27, 169–180.


[Package AICcmodavg version 1.05 Index]